Deep learning for the identification of autism traits in children through facial expressions: a systematic review - Palomino, Daniella Romani; Ovalle, Christian; Cordova-Berona, Heli (International Journal of Electrical and Computer Engineering (IJECE), 2025)
This study employs a bibliometric analysis to examine research on the application of artificial intelligence, specifically deep learning, in the detection of autism traits through facial expressions. Using quantitative methodologies. The analysis revealed a notable growth in scientific output from 2019, with an emphasis on techniques such as convolutional neural networks and systems based on the facial action coding system (FACS- CNN). The results highlight improvements in diagnostic accuracy thanks to the use of deep learning, although challenges related to data quality and availability remain. This study underscores the importance of international collaboration and technological innovation to advance the diagnosis and treatment of autism, offering a comprehensive perspective on current and future trends in this interdisciplinary field.